{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 获取数据的时间范围\n",
    "now = datetime.datetime.now()\n",
    "end_date1 = now.strftime('%Y%m%d')\n",
    "\n",
    "# 获取数据的时间范围，基于十年国债收益率的开始时间和结束时间\n",
    "yield_data_ary = [\n",
    "    ak.bond_china_yield(start_date='20130101', end_date='20131231'),\n",
    "    ak.bond_china_yield(start_date='20140101', end_date='20141231'),\n",
    "    ak.bond_china_yield(start_date='20150101', end_date='20151231'),\n",
    "    ak.bond_china_yield(start_date='20160101', end_date='20161231'),\n",
    "    ak.bond_china_yield(start_date='20170101', end_date='20171231'),\n",
    "    ak.bond_china_yield(start_date='20180101', end_date='20181231'),\n",
    "    ak.bond_china_yield(start_date='20190101', end_date='20191231'),\n",
    "    ak.bond_china_yield(start_date='20200101', end_date='20201231'),\n",
    "    ak.bond_china_yield(start_date='20210101', end_date='20211231'),\n",
    "    ak.bond_china_yield(start_date='20220101', end_date='20221231'),\n",
    "    ak.bond_china_yield(start_date='20230101', end_date='20231231'),\n",
    "    ak.bond_china_yield(start_date='20240101', end_date='20241231'),\n",
    "    ak.bond_china_yield(start_date='20250101', end_date=end_date1)\n",
    "]\n",
    "bond_yield_data = pd.concat(yield_data_ary, ignore_index=True)\n",
    "yield_data = bond_yield_data.loc[bond_yield_data['曲线名称'] == '中债国债收益率曲线', ['日期', '10年']]\n",
    "yield_data['日期'] = pd.to_datetime(yield_data['日期'])\n",
    "yield_data.set_index('日期', inplace=True)\n",
    "\n",
    "# 以十年国债收益率的开始时间和结束时间为主\n",
    "start_date = yield_data.index.min().strftime('%Y-%m-%d')\n",
    "end_date = yield_data.index.max().strftime('%Y-%m-%d')\n",
    "\n",
    "# 获取上证指数数据\n",
    "index_data = ak.stock_zh_index_daily(symbol=\"sh000001\")\n",
    "index_data['date'] = pd.to_datetime(index_data['date'])\n",
    "index_data = index_data[(index_data['date'] >= start_date) & (index_data['date'] <= end_date)]\n",
    "index_data = index_data[['date', 'close']]\n",
    "\n",
    "# 获取黄金价格数据\n",
    "gold_data = ak.futures_main_sina(symbol=\"AU0\", start_date=\"20130101\", end_date=end_date)\n",
    "gold_data['日期'] = pd.to_datetime(gold_data['日期'])\n",
    "gold_data = gold_data[['日期', '收盘价']]\n",
    "\n",
    "# 合并三个数据集\n",
    "data = pd.merge(index_data, gold_data, left_on='date', right_on='日期', suffixes=('_index', '_gold'))\n",
    "data = pd.merge(data, yield_data, left_on='date', right_index=True, how='left')\n",
    "\n",
    "# 计算每天的涨跌幅\n",
    "data['index_pct_change'] = data['close'].pct_change() * 100\n",
    "data['gold_pct_change'] = data['收盘价'].pct_change() * 100\n",
    "data['yield_pct_change'] = data['10年'].pct_change() * 100\n",
    "\n",
    "# 创建涨跌分类函数\n",
    "def classify_change(x):\n",
    "    if x > 0:\n",
    "        return '上涨'\n",
    "    elif x < 0:\n",
    "        return '下跌'\n",
    "    else:\n",
    "        return '平盘'\n",
    "\n",
    "# 将涨跌幅度归类\n",
    "data['index_change_class'] = data['index_pct_change'].apply(classify_change)\n",
    "data['gold_change_class'] = data['gold_pct_change'].apply(classify_change)\n",
    "\n",
    "# 使用净现值法计算国债价格变化\n",
    "def calculate_price_change(duration, current_price, yield_change):\n",
    "    \"\"\"\n",
    "    使用久期计算国债价格的变动\n",
    "    :param duration: 国债久期（年）\n",
    "    :param current_price: 当前价格\n",
    "    :param yield_change: 收益率变动（百分比）\n",
    "    :return: 国债价格的变化\n",
    "    \"\"\"\n",
    "    # 将收益率变化从百分比转换为小数\n",
    "    yield_change_decimal = yield_change / 100\n",
    "    # 使用久期法计算价格变化\n",
    "    price_change = -duration * current_price * yield_change_decimal\n",
    "    return price_change\n",
    "\n",
    "# 假设当前价格为100元，久期为10年\n",
    "current_price = 100\n",
    "duration = 10  # 假设久期为10年\n",
    "\n",
    "# 创建 27 种情况的分类字典\n",
    "categories = ['上涨', '下跌', '平盘']\n",
    "results = {}\n",
    "\n",
    "# 遍历数据并统计每种组合的次数，并计算国债价格变化\n",
    "for _, row in data.iterrows():\n",
    "    index_class = row['index_change_class']\n",
    "    gold_class = row['gold_change_class']\n",
    "    yield_pct_change = row['yield_pct_change']\n",
    "    \n",
    "    # 计算国债价格变化\n",
    "    price_change = calculate_price_change(duration, current_price, yield_pct_change)\n",
    "    \n",
    "    # 将国债收益率的涨跌转换为国债价格涨跌\n",
    "    yield_class = classify_change(price_change)\n",
    "    \n",
    "    # 创建组合键\n",
    "    key = f\"上证-{index_class}_黄金-{gold_class}_国债价格-{yield_class}\"\n",
    "    \n",
    "    # 更新字典中的次数\n",
    "    if key not in results:\n",
    "        results[key] = {'count': 1}\n",
    "    else:\n",
    "        results[key]['count'] += 1\n",
    "\n",
    "# 创建柱状图\n",
    "labels = list(results.keys())\n",
    "counts = [value['count'] for value in results.values()]\n",
    "\n",
    "# 排序：按次数从高到低\n",
    "sorted_indices = np.argsort(counts)[::-1]\n",
    "labels_sorted = [labels[i] for i in sorted_indices]\n",
    "counts_sorted = [counts[i] for i in sorted_indices]\n",
    "\n",
    "# 绘制柱状图\n",
    "fig, ax1 = plt.subplots(figsize=(14, 7))\n",
    "bars = ax1.bar(labels_sorted, counts_sorted, color='lightblue')\n",
    "\n",
    "# 添加次数的标签\n",
    "for bar, count in zip(bars, counts_sorted):\n",
    "    yval = bar.get_height()\n",
    "    ax1.text(bar.get_x() + bar.get_width() / 2, yval + 0.1, f\"{count}\", ha='center', va='bottom')\n",
    "\n",
    "# 设置图表标题和标签\n",
    "ax1.set_xlabel('组合情况')\n",
    "ax1.set_ylabel('次数')\n",
    "ax1.set_title('上证指数、黄金和国债收益率涨跌情况统计（按次数排序）')\n",
    "plt.xticks(rotation=90)  # 旋转x轴标签，使其不重叠\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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